{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# notes\n",
    "* from now on I will focus on using `seaborn`. should be more than enoough for simple EDA purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exam1</th>\n",
       "      <th>exam2</th>\n",
       "      <th>admitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34.623660</td>\n",
       "      <td>78.024693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.286711</td>\n",
       "      <td>43.894998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.847409</td>\n",
       "      <td>72.902198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.182599</td>\n",
       "      <td>86.308552</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.032736</td>\n",
       "      <td>75.344376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       exam1      exam2  admitted\n",
       "0  34.623660  78.024693         0\n",
       "1  30.286711  43.894998         0\n",
       "2  35.847409  72.902198         0\n",
       "3  60.182599  86.308552         1\n",
       "4  79.032736  75.344376         1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('ex2data1.txt', names=['exam1', 'exam2', 'admitted'])\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>exam1</th>\n",
       "      <th>exam2</th>\n",
       "      <th>admitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>65.644274</td>\n",
       "      <td>66.221998</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.458222</td>\n",
       "      <td>18.582783</td>\n",
       "      <td>0.492366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.058822</td>\n",
       "      <td>30.603263</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.919511</td>\n",
       "      <td>48.179205</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>67.032988</td>\n",
       "      <td>67.682381</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>80.212529</td>\n",
       "      <td>79.360605</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.827858</td>\n",
       "      <td>98.869436</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            exam1       exam2    admitted\n",
       "count  100.000000  100.000000  100.000000\n",
       "mean    65.644274   66.221998    0.600000\n",
       "std     19.458222   18.582783    0.492366\n",
       "min     30.058822   30.603263    0.000000\n",
       "25%     50.919511   48.179205    0.000000\n",
       "50%     67.032988   67.682381    1.000000\n",
       "75%     80.212529   79.360605    1.000000\n",
       "max     99.827858   98.869436    1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x115491da0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QHgzDyEEpGsM00RXSUSJLGKFIUCURblHECFmEfGQpmmVwiOzD8lnTV199NQBgzZo1OHjw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\n6EkEcHF22YUEiLBLvXQjMFR9YdAMDFU7Txy65DYCAbyjA2g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y8vI0ffp0eTweTZw40VY1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\nAABgQkgEAACACSERAAAAJoREAAAAmPw/9Y26w6SRMccAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154915c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(context=\"notebook\", style=\"darkgrid\", palette=sns.color_palette(\"RdBu\", 2))\n",
    "\n",
    "sns.lmplot('exam1', 'exam2', hue='admitted', data=df, \n",
    "           size=6, \n",
    "           fit_reg=False, \n",
    "           scatter_kws={\"s\": 50}\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:tf]",
   "language": "python",
   "name": "conda-env-tf-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
